Although the performance of related detection models has surpassed the human level[1]}, training these deep models requires a large amount of labeled data, and these models can only identify the target classes included in the training set, and lack the ability to identify target classes that are not included in the training set[2]}.The setting of fully supervised learning is unrealistic in most industrial problems. Through communication with companies, we learned that it is difficult to collect a large number of defective samples during product production or use, and even if the collection is completed, the relevant models need to be retrained so that the entire process is costly. At the same time, the product may produce defects that have not occurred in the past.
Recently, Lampert et al.[3]} proposed a Zero-shot Learning(ZSL) method that is very different from traditional image classification tasks. They used different types of animal images in the training phase and the test phase, and proposed DAP and IAP using the semantic attributes shared by the training set (also called unseen classes) and the test set (also called seen class) as a bridge, where the semantic attribute represents the feature of a target instance or a certain latent attribute, and it can be a specific text description, or it can be a feature vector that does not have semantic interpretation. The method of DAP uses the image features of the training set to learn a classifier for each attribute, thereby learning the relationship between image features and attributes, and in the test stage, the classifier is used to predict the attributes of other animals, and then the new animals in the test set are judged by comparing with the attributes in the attribute library. The method of IAP is to first estimate the probability of whether the picture belongs to the known class, and then use the relationship between the attributes contained in the seen class and the unseen class to predict the test sample.
